How to Achieve High-Performance, Scalable and Distributed DNN Training on Modern HPC Systems?
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The document discusses the integration of high-performance computing (HPC), big data, and deep learning, emphasizing the importance of scalable and distributed training for deep neural networks (DNNs) on modern HPC systems. It outlines key challenges and advancements in deep learning frameworks, including the need for efficient communication and the role of specific MPI libraries like MVAPICH2 in facilitating this process. The presentation details various architectures and techniques used to optimize training times, alongside highlighting the performance results achieved with different deep learning frameworks.
How to Achieve High-Performance, Scalable and Distributed DNN Training on Modern HPC Systems?
- 1. How to Achieve High-Performance, Scalable and Distributed Training on Modern HPC Systems? Dhabaleswar K. (DK) Panda The Ohio State University E-mail: [email protected] http://www.cse.ohio-state.edu/~panda Talk at HPCAC-AI Stanford Conference (April ’20) by Follow us on https://twitter.com/mvapich
- 2. HPCAC-AI-Stanford (April ‘20) 2Network Based Computing Laboratory Understanding the Deep Learning Resurgence Adopted from: http://www.deeplearningbook.org/contents/intro.html • Deep Learning (DL) is a sub-set of Machine Learning (ML) – Perhaps, the most revolutionary subset! – Feature extraction vs. hand-crafted features • Deep Learning – A renewed interest and a lot of hype! – Key success: Deep Neural Networks (DNNs) – Everything was there since the late 80s except the “computability of DNNs” AI Machine Learning Deep Learning Examples: MLPs, DNNs, Examples: Logistic Regression
- 3. HPCAC-AI-Stanford (April ‘20) 3Network Based Computing Laboratory • Modern and efficient hardware enabled – Computability of DNNs – impossible in the past! – GPUs – at the core of DNN training – CPUs – catching up fast • Availability of Datasets – MNIST, CIFAR10, ImageNet, and more… • Excellent Accuracy for many application areas – Vision, Machine Translation, and several others... Deep Learning in the Many-core Era Courtesy: A. Canziani et al., “An Analysis of Deep Neural Network Models for Practical Applications”, CoRR, 2016. 0 2000 4000 6000 8000 10000 2 GTX 580 DGX-2 MinutestoTrain AlexNet ~500X in 5 years
- 4. HPCAC-AI-Stanford (April ‘20) 4Network Based Computing Laboratory Big Data (Hadoop, Spark, HBase, Memcached, etc.) Deep Learning (Caffe, TensorFlow, BigDL, etc.) HPC (MPI, RDMA, Lustre, etc.) Increasing Usage of HPC, Big Data and Deep Learning Convergence of HPC, Big Data, and Deep Learning! Increasing Need to Run these applications on the Cloud!!
- 5. HPCAC-AI-Stanford (April ‘20) 5Network Based Computing Laboratory Drivers of Modern HPC Cluster Architectures • Multi-core/many-core technologies • Remote Direct Memory Access (RDMA)-enabled networking (InfiniBand and RoCE) • Solid State Drives (SSDs), Non-Volatile Random-Access Memory (NVRAM), NVMe-SSD • Accelerators (NVIDIA GPGPUs and Intel Xeon Phi) • Available on HPC Clouds, e.g., Amazon EC2, NSF Chameleon, Microsoft Azure, etc. Accelerators / Coprocessors high compute density, high performance/watt >1 TFlop DP on a chip High Performance Interconnects - InfiniBand <1usec latency, 200Gbps Bandwidth>Multi-core Processors SSD, NVMe-SSD, NVRAM K - ComputerSunway TaihuLightSummit Sierra
- 6. HPCAC-AI-Stanford (April ‘20) 6Network Based Computing Laboratory • Deep Learning has two major tasks 1. Training of the Deep Neural Network 2. Inference (or deployment) that uses a trained DNN • DNN Training – Training is a compute/communication intensive process – can take days to weeks – Faster training is necessary! • Faster training can be achieved by – Using Newer and Faster Hardware – But, there is a limit! – Can we use more GPUs or nodes? • The need for Parallel and Distributed Training Key Phases of Deep Learning
- 7. HPCAC-AI-Stanford (April ‘20) 7Network Based Computing Laboratory • Scale-up: Intra-node Communication – Many improvements like: • NVIDIA cuDNN, cuBLAS, NCCL, etc. • CUDA 9 Co-operative Groups • Scale-out: Inter-node Communication – DL Frameworks – most are optimized for single-node only – Distributed (Parallel) Training is an emerging trend • OSU-Caffe – MPI-based • Microsoft CNTK – MPI/NCCL2 • Google TensorFlow – gRPC-based/MPI/NCCL2 • Facebook Caffe2 – Hybrid (NCCL2/Gloo/MPI) Scale-up and Scale-out Scale-upPerformance Scale-out Performance cuDNN gRPC Hadoop MPI MKL-DNN Desired NCCL2
- 8. HPCAC-AI-Stanford (April ‘20) 8Network Based Computing Laboratory Holistic Evaluation is Important!! • My framework is faster than your framework! • This needs to be understood in a holistic way. • Performance depends on the entire execution environment (the full stack) • Isolated view of performance is not helpful A. A. Awan, H. Subramoni, and Dhabaleswar K. Panda. “An In-depth Performance Characterization of CPU- and GPU-based DNN Training on Modern Architectures”, In Proceedings of the Machine Learning on HPC Environments (MLHPC'17). ACM, New York, NY, USA, Article 8. MKL/ MKL-DNN
- 9. HPCAC-AI-Stanford (April ‘20) 9Network Based Computing Laboratory How to efficiently scale-out a Deep Learning (DL) framework and take advantage of heterogeneous High Performance Computing (HPC) resources? Broad Challenge: Exploiting HPC for Deep Learning
- 10. HPCAC-AI-Stanford (April ‘20) 10Network Based Computing Laboratory 1. What are the fundamental issues in designing DL frameworks? – Memory Requirements – Computation Requirements – Communication Overhead 2. Why do we need to support distributed training? – To overcome the limits of single-node training – To better utilize hundreds of existing HPC Clusters Research Challenges to Exploit HPC Technologies InfiniBand GPUCPU CNTK Gradient Aggregation Model Propagation Forward Backward Deep Learning and Machine Learning Frameworks Caffe/ OSU-Caffe Caffe2 TensorFlow MXNet Communication Runtimes to support Distributed Training HPC Platforms Major Computation and Communication Phases in DL Frameworks 1 2
- 11. HPCAC-AI-Stanford (April ‘20) 11Network Based Computing Laboratory 3. What are the new design challenges brought forward by DL frameworks for Communication runtimes? – Large Message Collective Communication and Reductions – GPU Buffers (CUDA-Awareness) 4. Can a Co-design approach help in achieving Scale-up and Scale-out efficiently? – Co-Design the support at Runtime level and Exploit it at the DL Framework level – What performance benefits can be observed? – What needs to be fixed at the communication runtime layer? Research Challenges to Exploit HPC Technologies (Cont’d) CUDA- Awareness InfiniBand GPUCPU Large-message Collectives CNTK Point-to- Point Operations Gradient Aggregation Model Propagation Forward Backward Deep Learning and Machine Learning Frameworks Caffe/ OSU-Caffe Caffe2 TensorFlow MXNet Communication Runtimes (MPI/NCCL/Gloo/MLSL) HPC Platforms Major Computation and Communication Phases in DL Frameworks 3 4 Co-Design Opportunities
- 12. HPCAC-AI-Stanford (April ‘20) 12Network Based Computing Laboratory • MPI-driven Deep Learning – CPU-based Deep Learning – GPU-based Deep Learning • Co-designing Deep Learning Stacks with High-Performance MPI • Out-of-core DNN training • Exploiting Hybrid (Data and Model) Parallelism • Use-Case: AI-Driven Digital Pathology Multiple Approaches taken up by OSU
- 13. HPCAC-AI-Stanford (April ‘20) 13Network Based Computing Laboratory Data Parallel Deep Learning and MPI Collectives MPI_Bcast (GPU 0) packed_comm_buff L1 L2 .. Ln F L1 L2 .. Ln L1 L2 .. Ln L1 L2 .. Ln Params GPU0 Params GPU1 Params GPU2 Params GPU3 Gradients 1. Data Propagation 2. Forward Backward Pass 3. Gradient Aggregatio n B F B F B F B packed_red uce_buff packed_red uce_buff packed_red uce_buff packed_red uce_buff ApplyUpdates MPI_Reduce (GPU 0) Loop {} • Major MPI Collectives involved in Designing distributed frameworks • MPI_Bcast – required for DNN parameter exchange • MPI_Reduce – needed for gradient accumulation from multiple solvers • MPI_Allreduce – use just one Allreduce instead of Reduce and Broadcast A. A. Awan, K. Hamidouche, J. M. Hashmi, and D. K. Panda, S-Caffe: Co-designing MPI Runtimes and Caffe for Scalable Deep Learning on Modern GPU Clusters. In Proceedings of the 22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP '17)
- 14. HPCAC-AI-Stanford (April ‘20) 14Network Based Computing Laboratory Overview of the MVAPICH2 Project • High Performance open-source MPI Library for InfiniBand, Omni-Path, Ethernet/iWARP, and RDMA over Converged Ethernet (RoCE) – MVAPICH (MPI-1), MVAPICH2 (MPI-2.2 and MPI-3.1), Started in 2001, First version available in 2002 – MVAPICH2-X (MPI + PGAS), Available since 2011 – Support for GPGPUs (MVAPICH2-GDR) and MIC (MVAPICH2-MIC), Available since 2014 – Support for Virtualization (MVAPICH2-Virt), Available since 2015 – Support for Energy-Awareness (MVAPICH2-EA), Available since 2015 – Support for InfiniBand Network Analysis and Monitoring (OSU INAM) since 2015 – Used by more than 3,075 organizations in 89 countries – More than 732,000 (> 0.7 million) downloads from the OSU site directly – Empowering many TOP500 clusters (November ‘19 ranking) • 3rd ranked 10,649,640-core cluster (Sunway TaihuLight) at NSC, Wuxi, China • 5th, 448, 448 cores (Frontera) at TACC • 8th, 391,680 cores (ABCI) in Japan • 14th, 570,020 cores (Nurion) in South Korea and many others – Available with software stacks of many vendors and Linux Distros (RedHat, SuSE, OpenHPC, and Spack) – http://mvapich.cse.ohio-state.edu • Empowering Top500 systems for over a decade Partner in the 5th ranked TACC Frontera System
- 15. HPCAC-AI-Stanford (April ‘20) 15Network Based Computing Laboratory 0 100000 200000 300000 400000 500000 600000 700000 800000 Sep-04 Mar-05 Sep-05 Mar-06 Sep-06 Mar-07 Sep-07 Mar-08 Sep-08 Mar-09 Sep-09 Mar-10 Sep-10 Mar-11 Sep-11 Mar-12 Sep-12 Mar-13 Sep-13 Mar-14 Sep-14 Mar-15 Sep-15 Mar-16 Sep-16 Mar-17 Sep-17 Mar-18 Sep-18 Mar-19 Sep-19 NumberofDownloads Timeline MV0.9.4 MV20.9.0 MV20.9.8 MV21.0 MV1.0 MV21.0.3 MV1.1 MV21.4 MV21.5 MV21.6 MV21.7 MV21.8 MV21.9 MV2-GDR2.0b MV2-MIC2.0 MV2-GDR2.3.2 MV2-X2.3rc3 MV2Virt2.2 MV22.3.3 OSUINAM0.9.5 MV2-Azure2.3.2 MV2-AWS2.3 MVAPICH2 Release Timeline and Downloads
- 16. HPCAC-AI-Stanford (April ‘20) 16Network Based Computing Laboratory Architecture of MVAPICH2 Software Family (HPC and DL) High Performance Parallel Programming Models Message Passing Interface (MPI) PGAS (UPC, OpenSHMEM, CAF, UPC++) Hybrid --- MPI + X (MPI + PGAS + OpenMP/Cilk) High Performance and Scalable Communication Runtime Diverse APIs and Mechanisms Point-to- point Primitives Collectives Algorithms Energy- Awareness Remote Memory Access I/O and File Systems Fault Tolerance Virtualization Active Messages Job Startup Introspection & Analysis Support for Modern Networking Technology (InfiniBand, iWARP, RoCE, Omni-Path, Elastic Fabric Adapter) Support for Modern Multi-/Many-core Architectures (Intel-Xeon, OpenPOWER, Xeon-Phi, ARM, NVIDIA GPGPU) Transport Protocols Modern Features RC SRD UD DC UMR ODP SR- IOV Multi Rail Transport Mechanisms Shared Memory CMA IVSHMEM Modern Features Optane* NVLink CAPI* * Upcoming XPMEM
- 17. HPCAC-AI-Stanford (April ‘20) 17Network Based Computing Laboratory • gRPC – Officially available and supported – Open-source – can be enhanced by others – Accelerated gRPC (add RDMA to gRPC) • gRPC+X – Use gRPC for bootstrap and rendezvous – Actual communication is in “X” – X MPI, Verbs, GPUDirect RDMA (GDR), etc. • No-gRPC – Baidu – the first one to use MPI Collectives for TF – Horovod – Use NCCL, or MPI, or any other future library (e.g. IBM DDL support recently added) Data Parallel Training with TensorFlow *Awan et al., “Scalable Distributed DNN Training using TensorFlow and CUDA-Aware MPI: Characterization, Designs, and Performance Evaluation ”, CCGrid ’19.
- 18. HPCAC-AI-Stanford (April ‘20) 18Network Based Computing Laboratory MVAPICH2 (MPI)-driven Infrastructure for ML/DL Training MVAPICH2-X for CPU-Based Training MVAPICH2-GDR for GPU-Based Training Horovod TensorFlow PyTorch MXNet ML/DL Applications
- 19. HPCAC-AI-Stanford (April ‘20) 19Network Based Computing Laboratory MVAPICH2 Software Family (CPU-Based Deep Learning) High-Performance Parallel Programming Libraries MVAPICH2 Support for InfiniBand, Omni-Path, Ethernet/iWARP, and RoCE MVAPICH2-X Advanced MPI features, OSU INAM, PGAS (OpenSHMEM, UPC, UPC++, and CAF), and MPI+PGAS programming models with unified communication runtime MVAPICH2-GDR Optimized MPI for clusters with NVIDIA GPUs and for GPU-enabled Deep Learning Applications MVAPICH2-Virt High-performance and scalable MPI for hypervisor and container based HPC cloud MVAPICH2-EA Energy aware and High-performance MPI MVAPICH2-MIC Optimized MPI for clusters with Intel KNC Microbenchmarks OMB Microbenchmarks suite to evaluate MPI and PGAS (OpenSHMEM, UPC, and UPC++) libraries for CPUs and GPUs Tools OSU INAM Network monitoring, profiling, and analysis for clusters with MPI and scheduler integration OEMT Utility to measure the energy consumption of MPI applications
- 20. HPCAC-AI-Stanford (April ‘20) 20Network Based Computing Laboratory Performance of CNTK with MVAPICH2-X on CPU-based Deep Learning 0 200 400 600 800 28 56 112 224 ExecutionTime(s) No. of Processes Intel MPI MVAPICH2 MVAPICH2-XPMEM CNTK AlexNet Training (B.S=default, iteration=50, ppn=28) 20% 9% • CPU-based training of AlexNet neural network using ImageNet ILSVRC2012 dataset • Advanced XPMEM-based designs show up to 20% benefits over Intel MPI (IMPI) for CNTK DNN training using All_Reduce • The proposed designs show good scalability with increasing system size Designing Efficient Shared Address Space Reduction Collectives for Multi-/Many-cores, J. Hashmi, S. Chakraborty, M. Bayatpour, H. Subramoni, and DK Panda, 32nd IEEE International Parallel & Distributed Processing Symposium (IPDPS '18), May 2018 Available since MVAPICH2-X 2.3rc1 release
- 21. HPCAC-AI-Stanford (April ‘20) 21Network Based Computing Laboratory Distributed TensorFlow on TACC Frontera (2,048 CPU nodes) • Scaled TensorFlow to 2048 nodes on Frontera using MVAPICH2 and IntelMPI • MVAPICH2 and IntelMPI give similar performance for DNN training • Report a peak of 260,000 images/sec on 2,048 nodes • On 2048 nodes, ResNet-50 can be trained in 7 minutes! A. Jain, A. A. Awan, H. Subramoni, DK Panda, “Scaling TensorFlow, PyTorch, and MXNet using MVAPICH2 for High-Performance Deep Learning on Frontera”, DLS ’19 (SC ’19 Workshop).
- 22. HPCAC-AI-Stanford (April ‘20) 22Network Based Computing Laboratory Scaling DL Frameworks using MVAPICH2-X on TACC Frontera • On single node, TensorFlow (TF) is 8% better than MXNet • TF (tf_cnn_benchmark) is 2.13x better than PyTorch • TensorFlow is 1.7x better than MXNet • TF (Keras) gives better performance compared to PyTorch and MXNet. A. Jain, A. A. Awan, H. Subramoni, DK Panda, “Scaling TensorFlow, PyTorch, and MXNet using MVAPICH2 for High-Performance Deep Learning on Frontera”, DLS ’19 (SC ’19 Workshop).
- 23. HPCAC-AI-Stanford (April ‘20) 23Network Based Computing Laboratory MVAPICH2 Software Family (GPU-Based Deep Learning) High-Performance Parallel Programming Libraries MVAPICH2 Support for InfiniBand, Omni-Path, Ethernet/iWARP, and RoCE MVAPICH2-X Advanced MPI features, OSU INAM, PGAS (OpenSHMEM, UPC, UPC++, and CAF), and MPI+PGAS programming models with unified communication runtime MVAPICH2-GDR Optimized MPI for clusters with NVIDIA GPUs and for GPU-enabled Deep Learning Applications MVAPICH2-Virt High-performance and scalable MPI for hypervisor and container based HPC cloud MVAPICH2-EA Energy aware and High-performance MPI MVAPICH2-MIC Optimized MPI for clusters with Intel KNC Microbenchmarks OMB Microbenchmarks suite to evaluate MPI and PGAS (OpenSHMEM, UPC, and UPC++) libraries for CPUs and GPUs Tools OSU INAM Network monitoring, profiling, and analysis for clusters with MPI and scheduler integration OEMT Utility to measure the energy consumption of MPI applications
- 24. HPCAC-AI-Stanford (April ‘20) 24Network Based Computing Laboratory At Sender: At Receiver: MPI_Recv(r_devbuf, size, …); inside MVAPICH2 • Standard MPI interfaces used for unified data movement • Takes advantage of Unified Virtual Addressing (>= CUDA 4.0) • Overlaps data movement from GPU with RDMA transfers High Performance and High Productivity MPI_Send(s_devbuf, size, …); GPU-Aware (CUDA-Aware) MPI Library: MVAPICH2-GPU
- 25. HPCAC-AI-Stanford (April ‘20) 25Network Based Computing Laboratory CUDA-Aware MPI: MVAPICH2-GDR 1.8-2.3.3 Releases • Support for MPI communication from NVIDIA GPU device memory • High performance RDMA-based inter-node point-to-point communication (GPU-GPU, GPU-Host and Host-GPU) • High performance intra-node point-to-point communication for multi-GPU adapters/node (GPU-GPU, GPU-Host and Host-GPU) • Taking advantage of CUDA IPC (available since CUDA 4.1) in intra-node communication for multiple GPU adapters/node • Optimized and tuned collectives for GPU device buffers • MPI datatype support for point-to-point and collective communication from GPU device buffers • Unified memory
- 26. HPCAC-AI-Stanford (April ‘20) 26Network Based Computing Laboratory 0 2000 4000 6000 1 2 4 8 16 32 64 128 256 512 1K 2K 4K Bandwidth(MB/s) Message Size (Bytes) GPU-GPU Inter-node Bi-Bandwidth MV2-(NO-GDR) MV2-GDR-2.3 0 1000 2000 3000 4000 1 2 4 8 16 32 64 128 256 512 1K 2K 4K Bandwidth(MB/s) Message Size (Bytes) GPU-GPU Inter-node Bandwidth MV2-(NO-GDR) MV2-GDR-2.3 0 10 20 30 0 1 2 4 8 16 32 64 128 256 512 1K 2K 4K 8K Latency(us) Message Size (Bytes) GPU-GPU Inter-node Latency MV2-(NO-GDR) MV2-GDR 2.3 MVAPICH2-GDR-2.3 Intel Haswell (E5-2687W @ 3.10 GHz) node - 20 cores NVIDIA Volta V100 GPU Mellanox Connect-X4 EDR HCA CUDA 9.0 Mellanox OFED 4.0 with GPU-Direct-RDMA 10x 9x Optimized MVAPICH2-GDR Design 1.85us 11X
- 27. HPCAC-AI-Stanford (April ‘20) 27Network Based Computing Laboratory MVAPICH2-GDR vs. NCCL2 – Allreduce Operation (DGX-2) • Optimized designs in MVAPICH2-GDR offer better/comparable performance for most cases • MPI_Allreduce (MVAPICH2-GDR) vs. ncclAllreduce (NCCL2) on 1 DGX-2 node (16 Volta GPUs) 1 10 100 1000 10000 Latency(us) Message Size (Bytes) MVAPICH2-GDR-2.3.3 NCCL-2.5 ~2.5X better Platform: Nvidia DGX-2 system (16 Nvidia Volta GPUs connected with NVSwitch), CUDA 9.2 0 5 10 15 20 25 30 35 40 45 50 8 16 32 64 128 256 512 1K 2K 4K 8K 16K 32K 64K 128K Latency(us) Message Size (Bytes) MVAPICH2-GDR-2.3.3 NCCL-2.5 ~4.7X better C.-H. Chu, P. Kousha, A. Awan, K. S. Khorassani, H. Subramoni and D. K. Panda, "NV-Group: Link-Efficient Reductions for Distributed Deep Learning on Modern Dense GPU Systems, " ICS-2020, Accepted to be Presented.
- 28. HPCAC-AI-Stanford (April ‘20) 28Network Based Computing Laboratory MVAPICH2-GDR: Enhanced MPI_Allreduce at Scale • Optimized designs in MVAPICH2-GDR offer better performance for most cases • MPI_Allreduce (MVAPICH2-GDR) vs. ncclAllreduce (NCCL2) up to 1,536 GPUs 0 1 2 3 4 5 6 32M 64M 128M 256M Bandwidth(GB/s) Message Size (Bytes) Bandwidth on 1,536 GPUs MVAPICH2-GDR-2.3.3 NCCL 2.5 1.7X better 0 50 100 150 200 250 300 350 400 450 4 16 64 256 1K 4K 16K Latency(us) Message Size (Bytes) Latency on 1,536 GPUs MVAPICH2-GDR-2.3.3 NCCL 2.5 1.6X better Platform: Dual-socket IBM POWER9 CPU, 6 NVIDIA Volta V100 GPUs, and 2-port InfiniBand EDR Interconnect 0 2 4 6 8 10 24 48 96 192 384 768 1536 Bandwidth(GB/s) Number of GPUs 128MB Message SpectrumMPI 10.3 OpenMPI 4.0.1 NCCL 2.5 MVAPICH2-GDR-2.3.3 1.7X better C.-H. Chu, P. Kousha, A. Awan, K. S. Khorassani, H. Subramoni and D. K. Panda, "NV-Group: Link-Efficient Reductions for Distributed Deep Learning on Modern Dense GPU Systems, " ICS-2020. Accepted to be Presented.
- 29. HPCAC-AI-Stanford (April ‘20) 29Network Based Computing Laboratory Scalable TensorFlow using Horovod and MVAPICH2-GDR • ResNet-50 Training using TensorFlow benchmark on 1 DGX-2 node (16 Volta GPUs) 0 1000 2000 3000 4000 5000 6000 7000 1 2 4 8 16 Imagepersecond Number of GPUs NCCL-2.5 MVAPICH2-GDR-2.3.3 9% higher Platform: Nvidia DGX-2 system, CUDA 9.2 0 20 40 60 80 100 1 2 4 8 16 ScalingEfficiency(%) Number of GPUs NCCL-2.5 MVAPICH2-GDR-2.3.3 Scaling Efficiency = Actual throughput Ideal throughput at scale × 100% C.-H. Chu, P. Kousha, A. Awan, K. S. Khorassani, H. Subramoni and D. K. Panda, "NV-Group: Link-Efficient Reductions for Distributed Deep Learning on Modern Dense GPU Systems, " ICS-2020. Accepted to be Presented.
- 30. HPCAC-AI-Stanford (April ‘20) 30Network Based Computing Laboratory Distributed TensorFlow on ORNL Summit (1,536 GPUs) • ResNet-50 Training using TensorFlow benchmark on SUMMIT -- 1536 Volta GPUs! • 1,281,167 (1.2 mil.) images • Time/epoch = 3.6 seconds • Total Time (90 epochs) = 3.6 x 90 = 332 seconds = 5.5 minutes! 0 50 100 150 200 250 300 350 400 1 2 4 6 12 24 48 96 192 384 768 1536 Imagepersecond Thousands Number of GPUs NCCL-2.5 MVAPICH2-GDR 2.3.3 Platform: The Summit Supercomputer (#1 on Top500.org) – 6 NVIDIA Volta GPUs per node connected with NVLink, CUDA 9.2 *We observed errors for NCCL2 beyond 96 GPUs MVAPICH2-GDR reaching ~0.35 million images per second for ImageNet-1k! ImageNet-1k has 1.2 million images
- 31. HPCAC-AI-Stanford (April ‘20) 31Network Based Computing Laboratory • LLNL/Lassen POWER9 CPU with 4 V100 GPUs/node • PyTorch is becoming a very important DL framework • Scaling PyTorch models with Horovod is simple • MVAPICH2-GDR provides better performance and scalability compared to NCCL2 Scaling PyTorch on LLNL/Lassen using MVAPICH2-GDR 0 5000 10000 15000 20000 25000 30000 35000 40000 45000 1 2 4 8 16 32 64 128 Images/sec(higherisbetter) No. of GPUs MVAPICH2-GDR NCCL2 Platform: The Lassen Supercomputer (#10 on Top500.org) – 4 NVIDIA Volta GPUs per node connected with NVLink, CUDA 10.1
- 32. HPCAC-AI-Stanford (April ‘20) 32Network Based Computing Laboratory • RTX 5000 are NVIDIA’s GPUs targeted for data centers • Different from GTX series – Supports GPUDirect RDMA (GDR) – Supports GDRCOPY • MVAPICH2-GDR offers good performance and reasonable scaling • Scaling is not as good as Lassen because – Nodes are connected with IB FDR – No NVLink between GPUs (only PCIe) Early Exploration of MXNet using MVAPICH2-GDR on TACC Frontera RTX GPU Nodes 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 1 2 4 8 16 32 64 Images/second(higherisbetter) No. of GPUs MVAPICH2-GDR Platform: TACC Frontera-RTX – 4 NVIDIA RTX 5000 GPUs per node, CUDA 10.1
- 33. HPCAC-AI-Stanford (April ‘20) 33Network Based Computing Laboratory • TACC Longhorn is like the LLNL/Lassen system (POWER9 with 4 V100 GPUs/node) • Some restrictions to keep in mind – TensorFlow 2.1 on POWER9 only built with CUDA 10.2 • Early results are promising – Good scaling up to 256 GPUs Early Exploration of TensorFlow 2.1 on TACC/Longhorn using MVAPICH2-GDR 0 10000 20000 30000 40000 50000 60000 70000 1 2 4 8 16 32 64 128 256 Images/sec(higherisbetter) No. of GPUs MVAPICH2-GDR Platform: The Longhorn System at TACC – 4 NVIDIA Volta GPUs per node connected with NVLink, CUDA 10.2
- 34. HPCAC-AI-Stanford (April ‘20) 34Network Based Computing Laboratory • MPI-driven Deep Learning – CPU-based Deep Learning – GPU-based Deep Learning • Co-designing Deep Learning Stacks with High-Performance MPI • Out-of-core DNN training • Exploiting Hybrid (Data and Model) Parallelism • Use-Case: AI-Driven Digital Pathology Multiple Approaches taken up by OSU
- 35. HPCAC-AI-Stanford (April ‘20) 35Network Based Computing Laboratory • What are the fundamental bottlenecks in the existing DL frameworks? • How to design a Scalable and Distributed Framework? – Achieve both Scale-up and Scale-out efficiently • What are the new requirements and expectations for Communication Runtimes? • Can a Co-design approach help in achieving better training performance? – Can a DL framework like Caffe be co-designed with an MPI runtime? • What is the impact of the co-design? – What performance benefits can be observed? At what levels? S-Caffe: Co-designing HPC and DL A. A. Awan, K. Hamidouche, J.M. Hashmi, DK Panda, “S-Caffe: Co-designing MPI Runtimes and Caffe for Scalable Deep Learning on Modern GPU Clusters”, ACM PPoPP ’17
- 36. HPCAC-AI-Stanford (April ‘20) 36Network Based Computing Laboratory • Caffe : A flexible and layered Deep Learning framework. • Benefits and Weaknesses – Multi-GPU Training within a single node – Performance degradation for GPUs across different sockets – Limited Scale-out • OSU-Caffe: MPI-based Parallel Training – Enable Scale-up (within a node) and Scale-out (across multi-GPU nodes) – Scale-out on 64 GPUs for training CIFAR-10 network on CIFAR-10 dataset – Scale-out on 128 GPUs for training GoogLeNet network on ImageNet dataset OSU-Caffe: Scalable Deep Learning 0 50 100 150 200 250 8 16 32 64 128 TrainingTime(seconds) No. of GPUs GoogLeNet (ImageNet) on 128 GPUs Caffe OSU-Caffe (1024) OSU-Caffe (2048) Invalid use case OSU-Caffe publicly available from http://hidl.cse.ohio-state.edu/
- 37. HPCAC-AI-Stanford (April ‘20) 37Network Based Computing Laboratory Out-of-core Training via CUDA Unified Memory • Large DNNs cannot be trained on GPUs due to memory limitation! – ResNet-50 -- current frameworks only allow small batch sizes – Next-generation models like Neural Machine Translation (NMT), AmoebaNet, etc. • Ridiculously large consists of billions of parameters – Can we design Out-of-core DNN training support using new software features in CUDA 8/9 and hardware mechanisms in Pascal/Volta GPUs? • General intuition is that managed allocations “will be” slow! – The proposed OC-Caffe (Out-of-Core Caffe) exploits the potential of CUDA Unified Memory. • OC-Caffe-Opt: up to 80% better than Intel-optimized CPU Caffe for ResNet-50 training on the Volta V100 GPU with CUDA9 and CUDNN7 A. Awan et al., OC-DNN: Exploiting Advanced Unified Memory Capabilities in CUDA 9 and Volta GPUs for Out-of-Core DNN Training, HiPC ’18
- 38. HPCAC-AI-Stanford (April ‘20) 38Network Based Computing Laboratory • Data-Parallelism– only for models that fit the memory • Out-of-core models – Deeper model Better accuracy but more memory required! • Model parallelism can work for out-of-core models! • Key Challenges – Model Partitioning is difficult for application programmers – Finding the right partition (grain) size is hard – cut at which layer and why? – Developing a practical system for model-parallelism • Redesign DL Framework or create additional layers? • Existing Communication middleware or extensions needed? HyPar-Flow: Hybrid and Model Parallelism for CPUs A. A. Awan, A. Jain, Q. Anthony, H. Subramoni, and DK Panda, “HyPar-Flow: Exploiting MPI and Keras for Hybrid Parallel Training of TensorFlow models”, ISC ‘20 (Accepted to be presented), https://arxiv.org/pdf/1911.05146.pdf
- 39. HPCAC-AI-Stanford (April ‘20) 39Network Based Computing Laboratory • ResNet-1001 with variable batch size • Approach: – 48 model-partitions for 56 cores – 512 model-replicas for 512 nodes – Total cores: 48 x 512 = 24,576 • Speedup – 253X on 256 nodes – 481X on 512 nodes • Scaling Efficiency – 98% up to 256 nodes – 93.9% for 512 nodes Out-of-core Training with HyPar-Flow (512 nodes on TACC Frontera) 481x speedup on 512 Intel Xeon Skylake nodes (TACC Frontera) A. A. Awan, A. Jain, Q. Anthony, H. Subramoni, and DK Panda, “HyPar-Flow: Exploiting MPI and Keras for Hybrid Parallel Training of TensorFlow models”, ISC ‘20 (Accepted to be presented), https://arxiv.org/pdf/1911.05146.pdf
- 40. HPCAC-AI-Stanford (April ‘20) 40Network Based Computing Laboratory • The field of Pathology (like many other medical disciplines) is moving Digital • Traditionally, a pathologist reviews a slide and carries out diagnosis based on prior knowledge and training • Experience matters • Can Deep Learning to be used to train thousands of slides and – Let the computer carry out the diagnosis – Narrow down the diagnosis and help a pathologist to make a final decision • Significant benefits in – Reducing diagnosis time – Making pathologists productive – Reducing health care cost AI-Driven Digital Pathology
- 41. HPCAC-AI-Stanford (April ‘20) 41Network Based Computing Laboratory • Pathology whole slide image (WSI) – Each WSI = 100,000 x 100,000 pixels – Can not fit in a single GPU memory – Tiles are extracted to make training possible • Two main problems with tiles – Restricted tile size because of GPU memory limitation – Smaller tiles loose structural information • Can we use Model Parallelism to train on larger tiles to get better accuracy and diagnosis? • Reduced training time significantly – 7.25 hours (1 node, 4 GPUs) -> 27 mins (32 nodes, 128 GPUs) Exploiting Model Parallelism in AI-Driven Digital Pathology Courtesy: https://blog.kitware.com/digital-slide- archive-large-image-and-histomicstk-open-source- informatics-tools-for-management-visualization-and- analysis-of-digital-histopathology-data/
- 42. HPCAC-AI-Stanford (April ‘20) 42Network Based Computing Laboratory • Scalable distributed training is getting important • Requires high-performance middleware designs while exploiting modern interconnects • Provided a set of solutions to achieve scalable distributed training – MPI (MVAPICH2)-driven solution with Horovod for TensorFlow, PyTorch and MXNet – Optimized collectives for CPU-based training – CUDA-aware MPI with optimized collectives for GPU-based training – Out-of-core training and Hybrid Parallelism • Will continue to enable the DL community to achieve scalability and high-performance for their distributed training Conclusions
- 43. HPCAC-AI-Stanford (April ‘20) 43Network Based Computing Laboratory • Supported through X-ScaleSolutions (http://x-scalesolutions.com) • Benefits: – Help and guidance with installation of the library – Platform-specific optimizations and tuning – Timely support for operational issues encountered with the library – Web portal interface to submit issues and tracking their progress – Advanced debugging techniques – Application-specific optimizations and tuning – Obtaining guidelines on best practices – Periodic information on major fixes and updates – Information on major releases – Help with upgrading to the latest release – Flexible Service Level Agreements • Support being provided to Lawrence Livermore National Laboratory (LLNL) and KISTI, Korea Commercial Support for MVAPICH2, HiBD, and HiDL Libraries
- 44. HPCAC-AI-Stanford (April ‘20) 44Network Based Computing Laboratory • High-performance solution for distributed training for your complex AI problems • Features: – Integrated package with TensorFlow, PyTorch, MXNet, Horovod, and MVAPICH2 MPI libraries – Targeted for both CPU-based and GPU-based Deep Learning Training – Integrated profiling support across the stacks – Support for x86 and OpenPOWER platforms – Support for InfiniBand, RoCE and NVLink Interconnects – Out-of-the-box optimal performance – One-click deployment and execution • Send an e-mail to [email protected] for free trial!! X-ScaleAI Product
- 45. HPCAC-AI-Stanford (April ‘20) 45Network Based Computing Laboratory Funding Acknowledgments Funding Support by Equipment Support by
- 46. HPCAC-AI-Stanford (April ‘20) 46Network Based Computing Laboratory Personnel Acknowledgments Current Students (Graduate) – A. Awan (Ph.D.) – M. Bayatpour (Ph.D.) – C.-H. Chu (Ph.D.) – J. Hashmi (Ph.D.) – A. Jain (Ph.D.) – K. S. Kandadi (Ph.D.) Past Students – A. Augustine (M.S.) – P. Balaji (Ph.D.) – R. Biswas (M.S.) – S. Bhagvat (M.S.) – A. Bhat (M.S.) – D. Buntinas (Ph.D.) – L. Chai (Ph.D.) – B. Chandrasekharan (M.S.) – S. Chakraborthy (Ph.D.) – N. Dandapanthula (M.S.) – V. Dhanraj (M.S.) – R. Rajachandrasekar (Ph.D.) – D. Shankar (Ph.D.) – G. Santhanaraman (Ph.D.) – A. Singh (Ph.D.) – J. Sridhar (M.S.) – S. Sur (Ph.D.) – H. Subramoni (Ph.D.) – K. Vaidyanathan (Ph.D.) – A. Vishnu (Ph.D.) – J. Wu (Ph.D.) – W. Yu (Ph.D.) – J. Zhang (Ph.D.) Past Research Scientist – K. Hamidouche – S. Sur – X. Lu Past Post-Docs – D. Banerjee – X. Besseron – H.-W. Jin – T. Gangadharappa (M.S.) – K. Gopalakrishnan (M.S.) – W. Huang (Ph.D.) – W. Jiang (M.S.) – J. Jose (Ph.D.) – S. Kini (M.S.) – M. Koop (Ph.D.) – K. Kulkarni (M.S.) – R. Kumar (M.S.) – S. Krishnamoorthy (M.S.) – K. Kandalla (Ph.D.) – M. Li (Ph.D.) – P. Lai (M.S.) – J. Liu (Ph.D.) – M. Luo (Ph.D.) – A. Mamidala (Ph.D.) – G. Marsh (M.S.) – V. Meshram (M.S.) – A. Moody (M.S.) – S. Naravula (Ph.D.) – R. Noronha (Ph.D.) – X. Ouyang (Ph.D.) – S. Pai (M.S.) – S. Potluri (Ph.D.) – Kamal Raj (M.S.) – K. S. Khorassani (Ph.D.) – P. Kousha (Ph.D.) – A. Quentin (Ph.D.) – B. Ramesh (Ph.D.) – S. Xu (Ph.D.) – J. Lin – M. Luo – E. Mancini Past Programmers – D. Bureddy – J. Perkins Current Research Specialist – J. Smith – S. Marcarelli – A. Ruhela – J. Vienne Current Post-doc – M. S. Ghazimeersaeed – K. Manian Current Students (Undergraduate) – V. Gangal (B.S.) – N. Sarkauskas (B.S.) Past Research Specialist – M. Arnold Current Research Scientist – A. Shafi – H. Subramoni – Q. Zhou (Ph.D.) – H. Wang
- 47. HPCAC-AI-Stanford (April ‘20) 47Network Based Computing Laboratory Thank You! Network-Based Computing Laboratory http://nowlab.cse.ohio-state.edu/ [email protected] The High-Performance MPI/PGAS Project http://mvapich.cse.ohio-state.edu/ The High-Performance Deep Learning Project http://hidl.cse.ohio-state.edu/ The High-Performance Big Data Project http://hibd.cse.ohio-state.edu/